Literature DB >> 30175503

Approaching protein design with multisite λ dynamics: Accurate and scalable mutational folding free energies in T4 lysozyme.

Ryan L Hayes1, Jonah Z Vilseck1, Charles L Brooks1,2.   

Abstract

The estimation of changes in free energy upon mutation is central to the problem of protein design. Modern protein design methods have had remarkable success over a wide range of design targets, but are reaching their limits in ligand binding and enzyme design due to insufficient accuracy in mutational free energies. Alchemical free energy calculations have the potential to supplement modern design methods through more accurate molecular dynamics based prediction of free energy changes, but suffer from high computational cost. Multisite λ dynamics (MSλD) is a particularly efficient and scalable free energy method with potential to explore combinatorially large sequence spaces inaccessible with other free energy methods. This work aims to quantify the accuracy of MSλD and demonstrate its scalability. We apply MSλD to the classic problem of calculating folding free energies in T4 lysozyme, a system with a wealth of experimental measurements. Single site mutants considering 32 mutations show remarkable agreement with experiment with a Pearson correlation of 0.914 and mean unsigned error of 1.19 kcal/mol. Multisite mutants in systems with up to five concurrent mutations spanning 240 different sequences show comparable agreement with experiment. These results demonstrate the promise of MSλD in exploring large sequence spaces for protein design.
© 2018 The Protein Society.

Entities:  

Keywords:  binding; catalysis; molecular dynamics; protein folding

Mesh:

Substances:

Year:  2018        PMID: 30175503      PMCID: PMC6225981          DOI: 10.1002/pro.3500

Source DB:  PubMed          Journal:  Protein Sci        ISSN: 0961-8368            Impact factor:   6.725


  77 in total

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2.  The SAMPL2 blind prediction challenge: introduction and overview.

Authors:  Matthew T Geballe; A Geoffrey Skillman; Anthony Nicholls; J Peter Guthrie; Peter J Taylor
Journal:  J Comput Aided Mol Des       Date:  2010-05-09       Impact factor: 3.686

3.  Random walk in orthogonal space to achieve efficient free-energy simulation of complex systems.

Authors:  Lianqing Zheng; Mengen Chen; Wei Yang
Journal:  Proc Natl Acad Sci U S A       Date:  2008-12-15       Impact factor: 11.205

4.  A blind challenge for computational solvation free energies: introduction and overview.

Authors:  J Peter Guthrie
Journal:  J Phys Chem B       Date:  2009-04-09       Impact factor: 2.991

5.  Accurate Modeling of Scaffold Hopping Transformations in Drug Discovery.

Authors:  Lingle Wang; Yuqing Deng; Yujie Wu; Byungchan Kim; David N LeBard; Dan Wandschneider; Mike Beachy; Richard A Friesner; Robert Abel
Journal:  J Chem Theory Comput       Date:  2016-12-09       Impact factor: 6.006

6.  Charge-leveling and proper treatment of long-range electrostatics in all-atom molecular dynamics at constant pH.

Authors:  Jason A Wallace; Jana K Shen
Journal:  J Chem Phys       Date:  2012-11-14       Impact factor: 3.488

7.  Accurate and Reliable Prediction of the Binding Affinities of Macrocycles to Their Protein Targets.

Authors:  Haoyu S Yu; Yuqing Deng; Yujie Wu; Dan Sindhikara; Amy R Rask; Takayuki Kimura; Robert Abel; Lingle Wang
Journal:  J Chem Theory Comput       Date:  2017-11-30       Impact factor: 6.006

8.  A de novo protein binding pair by computational design and directed evolution.

Authors:  John Karanicolas; Jacob E Corn; Irwin Chen; Lukasz A Joachimiak; Orly Dym; Sun H Peck; Shira Albeck; Tamar Unger; Wenxin Hu; Gaohua Liu; Scott Delbecq; Gaetano T Montelione; Clint P Spiegel; David R Liu; David Baker
Journal:  Mol Cell       Date:  2011-03-31       Impact factor: 17.970

9.  Adaptive Landscape Flattening Accelerates Sampling of Alchemical Space in Multisite λ Dynamics.

Authors:  Ryan L Hayes; Kira A Armacost; Jonah Z Vilseck; Charles L Brooks
Journal:  J Phys Chem B       Date:  2017-02-10       Impact factor: 2.991

10.  Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ(1) and χ(2) dihedral angles.

Authors:  Robert B Best; Xiao Zhu; Jihyun Shim; Pedro E M Lopes; Jeetain Mittal; Michael Feig; Alexander D Mackerell
Journal:  J Chem Theory Comput       Date:  2012-07-18       Impact factor: 6.006

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  6 in total

1.  Automated, Accurate, and Scalable Relative Protein-Ligand Binding Free-Energy Calculations Using Lambda Dynamics.

Authors:  E Prabhu Raman; Thomas J Paul; Ryan L Hayes; Charles L Brooks
Journal:  J Chem Theory Comput       Date:  2020-11-17       Impact factor: 6.006

2.  Mutant thermal proteome profiling for characterization of missense protein variants and their associated phenotypes within the proteome.

Authors:  Sarah A Peck Justice; Monica P Barron; Guihong D Qi; H R Sagara Wijeratne; José F Victorino; Ed R Simpson; Jonah Z Vilseck; Aruna B Wijeratne; Amber L Mosley
Journal:  J Biol Chem       Date:  2020-09-02       Impact factor: 5.157

3.  BLaDE: A Basic Lambda Dynamics Engine for GPU-Accelerated Molecular Dynamics Free Energy Calculations.

Authors:  Ryan L Hayes; Joshua Buckner; Charles L Brooks
Journal:  J Chem Theory Comput       Date:  2021-10-28       Impact factor: 6.578

4.  Correction Schemes for Absolute Binding Free Energies Involving Lipid Bilayers.

Authors:  Zhiyi Wu; Philip C Biggin
Journal:  J Chem Theory Comput       Date:  2022-03-22       Impact factor: 6.578

5.  Generalizing the Discrete Gibbs Sampler-Based λ-Dynamics Approach for Multisite Sampling of Many Ligands.

Authors:  Jonah Z Vilseck; Xinqiang Ding; Ryan L Hayes; Charles L Brooks
Journal:  J Chem Theory Comput       Date:  2021-06-08       Impact factor: 6.006

6.  Molecular dynamics simulations suggest stabilizing mutations in a de novo designed α/β protein.

Authors:  Matthew Gill; Michelle E McCully
Journal:  Protein Eng Des Sel       Date:  2019-12-31       Impact factor: 1.650

  6 in total

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